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Updated: May 6, 2026
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
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Online genetic databases informing human genome epidemiology
Angela J Frodsham1, Julian P T Higgins
1Public Health Genetics Unit, Strangeways Research Laboratory, Cambridge, UK. angelaf@mrc-bsu.cam.ac.uk
BMC Medical Research Methodology
|July 6, 2007
Summary
Online searches for genetic epidemiology data reveal a scarcity of gene-disease association studies. Most available online databases supplement, rather than replace, traditional literature searches for systematic reviews.
Area of Science:
- Genetics
- Epidemiology
- Bioinformatics
Background:
- Advancements in high-throughput genotyping and genomics projects (e.g., human genome sequencing, HapMap) generate vast amounts of genetic data relevant to disease risk.
- Systematic reviews and meta-analyses require efficient identification and data extraction from relevant human genome epidemiology studies.
- MEDLINE is a primary resource, but its supplementation by online databases for genetic epidemiological information was investigated.
Purpose of the Study:
- To identify online databases containing genetic epidemiological information on gene prevalence and gene-disease associations.
- To assess the supplementary value of these online databases for systematic reviews and meta-analyses.
Main Methods:
- A systematic search was conducted for online databases with genetic epidemiological information.
- Databases containing genetic association study information were analyzed for supplementary data beyond MEDLINE.
Main Results:
- 111 databases with prevalence data and 67 single-gene databases were identified.
- Only 13 databases contained gene-disease association information; most were linked to MEDLINE.
- Five of these databases offered unique information not readily available elsewhere.
Conclusions:
- A comprehensive, single online resource for structured genetic association study data across multiple diseases is currently lacking.
- The availability of web-based information specific to gene-disease association studies is limited relative to the volume of research.
- Without centralized data repositories, systematic review authors and meta-analysts face challenges accessing new genetic association data.
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